# coding=utf-8
# Copyright 2020, The T5 Authors and HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" T5 model configuration"""
from typing import Mapping

from transformers.configuration_utils import PretrainedConfig
from transformers.onnx import OnnxSeq2SeqConfigWithPast
from transformers.utils import logging


logger = logging.get_logger(__name__)

T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class T5Config(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`T5Model`] or a [`TFT5Model`]. It is used to
    instantiate a T5 model according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the T5
    [t5-small](https://huggingface.co/t5-small) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Arguments:
        vocab_size (`int`, *optional*, defaults to 32128):
            Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`].
        d_model (`int`, *optional*, defaults to 512):
            Size of the encoder layers and the pooler layer.
        d_kv (`int`, *optional*, defaults to 64):
            Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model //
            num_heads`.
        d_ff (`int`, *optional*, defaults to 2048):
            Size of the intermediate feed forward layer in each `T5Block`.
        num_layers (`int`, *optional*, defaults to 6):
            Number of hidden layers in the Transformer encoder.
        num_decoder_layers (`int`, *optional*):
            Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set.
        num_heads (`int`, *optional*, defaults to 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        relative_attention_num_buckets (`int`, *optional*, defaults to 32):
            The number of buckets to use for each attention layer.
        relative_attention_max_distance (`int`, *optional*, defaults to 128):
            The maximum distance of the longer sequences for the bucket separation.
        dropout_rate (`float`, *optional*, defaults to 0.1):
            The ratio for all dropout layers.
        layer_norm_eps (`float`, *optional*, defaults to 1e-6):
            The epsilon used by the layer normalization layers.
        initializer_factor (`float`, *optional*, defaults to 1):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).
        feed_forward_proj (`string`, *optional*, defaults to `"relu"`):
            Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. T5v1.1 uses the
            `"gated-gelu"` feed forward projection. Original T5 uses `"relu"`.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
    """
    model_type = "t5"
    keys_to_ignore_at_inference = ["past_key_values"]
    attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}

    def __init__(
            self,
            vocab_size=32128,
            d_model=512,
            d_kv=64,
            d_ff=2048,
            #############################
            expert_capacity=64,
            router_type="tokens_masked",
            router_bias=False,
            router_jitter_noise=0.01,
            router_z_loss_coef=0.001,
            router_aux_loss_coef=0.001,
            add_router_probs=False,
            router_dtype="float32",
            router_ignore_padding_tokens=False,
            num_experts=5,
            #############################
            num_layers=6,
            num_decoder_layers=None,
            num_heads=8,
            relative_attention_num_buckets=32,
            relative_attention_max_distance=128,
            dropout_rate=0.1,
            layer_norm_epsilon=1e-6,
            initializer_factor=1.0,
            feed_forward_proj="relu",
            is_encoder_decoder=True,
            use_cache=True,
            pad_token_id=0,
            eos_token_id=1,
            **kwargs
        ):
        self.vocab_size = vocab_size
        self.d_model = d_model
        self.d_kv = d_kv
        self.d_ff = d_ff

        self.num_layers = num_layers

        self.num_decoder_layers = (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry

        self.num_heads = num_heads
        self.relative_attention_num_buckets = relative_attention_num_buckets
        self.relative_attention_max_distance = relative_attention_max_distance
        self.dropout_rate = dropout_rate
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_factor = initializer_factor
        self.feed_forward_proj = feed_forward_proj
        self.use_cache = use_cache

        act_info = self.feed_forward_proj.split("-")
        self.dense_act_fn = act_info[-1]
        self.is_gated_act = act_info[0] == "gated"
        #######################################################################################

        self.num_layers = num_layers
        self.num_decoder_layers = (
            num_decoder_layers if num_decoder_layers is not None else self.num_layers
        )  # default = symmetry
        # This tells us, each how many encoder layer we'll have to set a sparse layer.

        self.num_heads = num_heads
        self.router_type = router_type
        self.num_experts = num_experts
        self.expert_capacity = expert_capacity
        self.router_bias = router_bias
        self.router_jitter_noise = router_jitter_noise
        if router_dtype not in ["float32", "float16", "bfloat16"]:
            raise ValueError(f"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}")
        self.router_dtype = router_dtype

        self.router_ignore_padding_tokens = router_ignore_padding_tokens
        self.relative_attention_num_buckets = relative_attention_num_buckets
        self.relative_attention_max_distance = relative_attention_max_distance

        self.dropout_rate = dropout_rate
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_factor = initializer_factor
        self.feed_forward_proj = feed_forward_proj
        self.use_cache = use_cache
        self.add_router_probs = add_router_probs

        self.router_z_loss_coef = router_z_loss_coef
        self.router_aux_loss_coef = router_aux_loss_coef

        act_info = self.feed_forward_proj.split("-")
        self.dense_act_fn = act_info[-1]
        self.is_gated_act = act_info[0] == "gated"
        #######################################################################################



        if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
            raise ValueError(
                f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
                "'gated-gelu' or 'relu'"
            )

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
            self.dense_act_fn = "gelu_new"

        super().__init__(
            pad_token_id=pad_token_id,
            eos_token_id=eos_token_id,
            is_encoder_decoder=is_encoder_decoder,
            **kwargs,
        )


class T5OnnxConfig(OnnxSeq2SeqConfigWithPast):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        common_inputs = {
            "input_ids": {0: "batch", 1: "encoder_sequence"},
            "attention_mask": {0: "batch", 1: "encoder_sequence"},
        }
        if self.use_past:
            common_inputs["attention_mask"][1] = "past_encoder_sequence + sequence"
            common_inputs["decoder_input_ids"] = {0: "batch"}
            common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
            common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
            common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
            self.fill_with_past_key_values_(common_inputs, direction="inputs")

        return common_inputs

    @property
    def default_onnx_opset(self) -> int:
        return 13

#     def __init__(
#         self,
#         vocab_size=32128,
#         d_model=512,
#         d_kv=64,
#         d_ff=2048,
#         num_layers=6,
#         num_decoder_layers=None,
#         num_heads=8,
#         relative_attention_num_buckets=32,
#         relative_attention_max_distance=128,
#         dropout_rate=0.1,
#         layer_norm_epsilon=1e-6,
#         initializer_factor=1.0,
#         feed_forward_proj="relu",
#         is_encoder_decoder=True,
#         use_cache=True,
#         pad_token_id=0,
#         eos_token_id=1,
#         **kwargs
#     ):
#         self.vocab_size = vocab_size
#         self.d_model = d_model
#         self.d_kv = d_kv
#         self.d_ff = d_ff
#         self.num_layers = num_layers
#         self.num_decoder_layers = (
#             num_decoder_layers if num_decoder_layers is not None else self.num_layers
#         )  # default = symmetry
#         self.num_heads = num_heads
#         self.relative_attention_num_buckets = relative_attention_num_buckets
#         self.relative_attention_max_distance = relative_attention_max_distance
#         self.dropout_rate = dropout_rate
#         self.layer_norm_epsilon = layer_norm_epsilon
#         self.initializer_factor = initializer_factor
#         self.feed_forward_proj = feed_forward_proj
#         self.use_cache = use_cache

#         act_info = self.feed_forward_proj.split("-")
#         self.dense_act_fn = act_info[-1]
#         self.is_gated_act = act_info[0] == "gated"

#         if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
#             raise ValueError(
#                 f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
#                 "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
#                 "'gated-gelu' or 'relu'"
#             )

#         # for backwards compatibility
#         if feed_forward_proj == "gated-gelu":
#             self.dense_act_fn = "gelu_new"

#         super().__init__(
#             pad_token_id=pad_token_id,
#             eos_token_id=eos_token_id,
#             is_encoder_decoder=is_encoder_decoder,
#             **kwargs,
#         )


# class T5OnnxConfig(OnnxSeq2SeqConfigWithPast):
#     @property
#     def inputs(self) -> Mapping[str, Mapping[int, str]]:
#         common_inputs = {
#             "input_ids": {0: "batch", 1: "encoder_sequence"},
#             "attention_mask": {0: "batch", 1: "encoder_sequence"},
#         }
#         if self.use_past:
#             common_inputs["attention_mask"][1] = "past_encoder_sequence + sequence"
#             common_inputs["decoder_input_ids"] = {0: "batch"}
#             common_inputs["decoder_attention_mask"] = {0: "batch", 1: "past_decoder_sequence + sequence"}
#         else:
#             common_inputs["decoder_input_ids"] = {0: "batch", 1: "decoder_sequence"}
#             common_inputs["decoder_attention_mask"] = {0: "batch", 1: "decoder_sequence"}

#         if self.use_past:
#             self.fill_with_past_key_values_(common_inputs, direction="inputs")

#         return common_inputs

#     @property
#     def default_onnx_opset(self) -> int:
#         return 13
